CN117353349A - Power supply state control method of energy storage system, storage medium and electronic equipment - Google Patents
Power supply state control method of energy storage system, storage medium and electronic equipment Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
Abstract
The invention discloses a power supply state control method of an energy storage system, a storage medium and electronic equipment. Wherein the method comprises the following steps: acquiring offline power data and real-time power data of load equipment of an energy storage system, wherein the offline power data are power data of the load equipment acquired in advance in a plurality of time periods, and the real-time power data are power data of the load equipment acquired in real time in a preset time window; predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after a preset time window; the power state of the energy storage system is controlled based on the target predicted power. The invention solves the technical problem of lower efficiency of controlling the power supply state of the energy storage system.
Description
Technical Field
The present invention relates to the field of energy storage system control, and in particular, to a power supply state control method of an energy storage system, a storage medium, and an electronic device.
Background
The energy storage system can store electric energy and can be used as a power supply side to supply the electric energy to the load equipment, and in practical application, in order to meet the power consumption requirement of the load equipment and reduce the waste of the electric energy, and maintain the stable operation of the power grid, the balance between the power supply power of the energy storage system and the power consumption power of the load equipment needs to be controlled as much as possible.
At present, in the related art, modes of reducing power supply to a power supply side or controlling power consumption of load equipment are mostly adopted, so that the operation of the load equipment is influenced, and a method for controlling the power supply state of an energy storage system is lacked, so that the efficiency of controlling the power supply state of the energy storage system is lower.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the invention provides a power supply state control method of an energy storage system, a storage medium and electronic equipment, which are used for at least solving the technical problem of low efficiency of controlling the power supply state of the energy storage system.
According to an aspect of an embodiment of the present invention, there is provided a power supply state control method of an energy storage system, including: acquiring offline power data and real-time power data of load equipment of an energy storage system, wherein the offline power data are power data of the load equipment acquired in advance in a plurality of time periods, and the real-time power data are power data of the load equipment acquired in real time in a preset time window; predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after a preset time window; the power state of the energy storage system is controlled based on the target predicted power.
Optionally, predicting the required power of the load device at the first time point based on the offline power data and the real-time power data to obtain the target predicted power, including: predicting the required power of the load equipment at a first time point based on the offline power data to obtain first predicted power; predicting the required power of the load equipment at a first time point based on the real-time power data to obtain second predicted power; and obtaining the target predicted power based on the weighted sum of the first predicted power and the second predicted power.
Optionally, predicting the required power of the load device at the first time point based on the offline power data, to obtain a first predicted power, including: dividing offline power data based on a plurality of time periods to obtain a data matrix; determining a plurality of historical powers corresponding to a second time point in the plurality of time periods based on the data matrix, wherein the second time point is a time point of the plurality of time periods corresponding to the first time point; and predicting the required power of the load equipment at a first time point based on the plurality of historical powers to obtain first predicted power.
Optionally, predicting the required power of the load device at the first time point based on the plurality of historical powers to obtain a first predicted power, including: determining a plurality of first sub-predicted powers based on the plurality of historical powers; and predicting the required power of the load equipment at a first time point based on the plurality of historical powers and the plurality of first sub-predicted powers to obtain first predicted power.
Optionally, predicting the required power of the load device at the first time point based on the real-time power data to obtain the second predicted power, including: determining a power sequence of the load equipment in a preset time window based on the real-time power data, wherein the power sequence comprises a plurality of real-time powers acquired according to a time sequence; determining a plurality of second sub-predicted powers based on the plurality of real-time powers; and predicting the required power of the load equipment at the first time point based on the plurality of real-time powers and the plurality of second sub-predicted powers to obtain second predicted power.
Optionally, controlling the power state of the energy storage system based on the target predicted power includes: determining a target difference value between preset power and target predicted power of the energy storage system; controlling the power supply state to be switched to a charging state in response to the target predicted power being greater than or equal to the preset power, and charging the energy storage system based on the target difference value or the maximum charging power of the energy storage system; and controlling the power supply state to be switched to a discharge state in response to the target predicted power being smaller than the preset power, and discharging the energy storage system based on the target difference value or the maximum discharge power of the energy storage system.
Optionally, charging the energy storage system based on the target difference or the maximum charging power of the energy storage system includes: in response to the target difference being greater than or equal to the maximum charging power, charging the energy storage system based on the maximum charging power; and in response to the target difference being less than the maximum charging power, charging the energy storage system based on the target difference.
Optionally, discharging the energy storage system based on the target difference or the maximum discharge power of the energy storage system, including: discharging the energy storage system based on the target difference value in response to the target difference value being less than the maximum discharge power; and responsive to the target difference being greater than or equal to the maximum charge power, discharging the energy storage system based on the maximum charge power.
According to another aspect of the embodiment of the present invention, there is also provided a power supply state control device of an energy storage system, including: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring offline power data and real-time power data of load equipment of an energy storage system, wherein the offline power data is power data of the load equipment acquired in advance in a plurality of time periods, and the real-time power data is power data of the load equipment acquired in real time in a preset time window; the prediction module is used for predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after a preset time window; and the control module is used for controlling the power supply state of the energy storage system based on the target predicted power.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable storage medium, including a stored program, where the power supply state control method of the energy storage system described above is executed in a processor of a device where the program is controlled to run.
According to another aspect of embodiments of the present invention, there is also provided an electronic device, one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the method for controlling the power state of the energy storage system.
In the embodiment of the invention, offline power data and real-time power data of load equipment of an energy storage system are acquired, wherein the offline power data are power data of the load equipment in a plurality of time periods acquired in advance, and the real-time power data are power data of the load equipment in a preset time window acquired in real time; predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after a preset time window; the power state of the energy storage system is controlled based on the target predicted power. According to the method and the device, through the offline power data and the real-time power data of the load equipment, the periodic electric energy demand power and the trending electric energy demand power of the load equipment at the first time point are predicted, then the target prediction power of the load equipment is predicted, the power supply state of the energy storage system is adjusted based on the target prediction power of the load equipment, the charging and discharging states of the energy storage equipment are accurately controlled based on the electric energy demand power of the load equipment, the electric energy is required to be supplied by the load equipment, the waste of electric energy caused by excessive supply of the energy storage system is avoided, the stable operation of a power grid is maintained, and the technical problem that the efficiency of the power supply state control of the energy storage system is lower is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a method of controlling a power state of an energy storage system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an alternative method of power state control of an energy storage system according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a data matrix of alternative load device power data in accordance with an embodiment of the invention;
FIG. 4 is a schematic diagram illustrating the effect of an alternative power state control method adjustment for an energy storage system according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a power state control device of an energy storage system according to an embodiment of the present application.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
According to an embodiment of the present invention, there is provided an embodiment of a power state control method of an energy storage system, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
Fig. 1 is a method for controlling a power supply state of an energy storage system according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, acquiring offline power data and real-time power data of load equipment of an energy storage system.
The offline power data are power data of load equipment collected in advance in a plurality of time periods, and the real-time power data are power data of the load equipment collected in real time in a preset time window.
The energy storage system may refer to a device that may store energy, where the energy storage system may include, but is not limited to, a battery energy storage system, a compressed air energy storage system, a water pump energy storage system, a super capacitor energy storage system, and the like.
The load device may be a device connected to the energy storage system, and the load device may consume the electrical energy provided by the energy storage system.
The offline power data may refer to periodic power consumption data in the historical power consumption data of the load device, that is, the offline power data is used to represent the historical required power of the load device, where the historical required power of the load device may refer to the required power of the load device at the same corresponding time point in multiple time periods, in this embodiment, the offline power data may be power consumption data of the load device at the same time in the last N days, for example, the offline power data may be power consumption data of the load device at the time of 17:00 days in the last 5 days, and the offline power data may be selected according to needs, which is not limited herein.
The real-time power data may refer to trending power consumption data in the historical power consumption data of the load device, that is, the real-time power data is used to represent the trending historical demand power of the load device, where the trending historical demand power of the load device may refer to demand power collected by the load device at continuous time points within a preset time window, in this embodiment, the real-time power data may refer to power consumption data of the load device collected within the preset time window, for example, the real-time power data may be power consumption data of the load device collected within 15min, and the real-time power data may be selected according to needs, which is not limited herein.
In an alternative embodiment, the data in the system may be stored in the file system by a data collection tool, for example, the data in the distributed message system Kafka may be stored in the distributed file system (Hadoop Distributed File System, HDFS for short) by a streaming data collection tool (Flume Data Collection Tool, for short), and examples of text fields stored in the distributed file system may be spliced together in a complete text data using "_as shown in the following table.
Examples of text fields stored in the distributed file system are shown in table 1 below:
in another alternative embodiment, the text data stored in the distributed file system may be calculated by a distributed data processing framework (Spark) task using a day as a cycle period by using a first-order exponential smoothing algorithm on the data of the last N days, so as to obtain periodic power consumption data of the load device at the same time and different days, or may obtain offline power data of the load device in other manners, which is not limited herein.
In an alternative embodiment, the acquiring device may acquire the real-time power data of the load device according to the preset time window T0, and send the acquired real-time power data to the Kafka message queue cluster in the data collecting module, so as to acquire the real-time power data of the load device, or may acquire the real-time power data of the load device in other manners, which is not limited herein.
Step S104, the required power of the load equipment at the first time point is predicted based on the offline power data and the real-time power data, and the target predicted power is obtained.
The first time point is a time point after a preset time window.
The first time point may be a time when the electric power demand power needs to be predicted, and the first time point is denoted as a t+1 time, and the first time point may be selected according to needs, which is not limited herein.
The target predicted power may refer to the electrical energy demand power of the load device at the first time point, which needs to be predicted.
In an alternative embodiment, the periodic power demand of the load device at the first point in time may be predicted based on the offline power data, the trending power demand of the load device at the first point in time may be predicted based on the real-time power data, and the target predicted power may be determined based on the periodic power demand of the load device at the first point in time and the trending power demand.
Step S106, controlling the power supply state of the energy storage system based on the target predicted power.
The power supply state may refer to a state of the energy storage system, and since the energy storage system may store electric energy and supply electric energy to the load device, the power supply state of the energy storage system may include, but is not limited to, charging the energy storage system with a certain power and discharging the energy storage system with a certain power.
In an alternative embodiment, since the energy storage system may store electric energy and supply electric energy to the load device, the above steps have determined the electric energy demand power of the load device at the first time point, and in order to meet the electric energy demand of the load device and avoid the waste of electric energy caused by the excessive supply of the energy storage system, the power supply state of the energy storage system may be adjusted based on the target predicted power of the load device.
When the target predicted power of the load device is smaller than the preset electric energy power which the energy storage system should supply to the load device, namely, the energy storage system can meet the electric energy demand of the load device, in order to avoid waste of electric energy caused by excessive supply of the energy storage system, the energy storage system can be controlled to be charged with a certain power, so that the energy storage system can temporarily store the electric energy.
When the target predicted power of the load equipment is larger than or equal to the preset electric energy power which the energy storage system should supply to the load equipment, namely the energy storage system cannot meet the electric energy requirement of the load equipment, the energy storage system can be controlled to discharge with a certain power, and the energy storage system can release part of stored electric energy so as to meet the electric energy requirement of the load equipment.
Fig. 2 is a schematic diagram of an alternative power supply state control method of an energy storage system according to an embodiment of the present invention, as shown in fig. 2, a data collection module may obtain offline power data and real-time power data of a load device of the energy storage system, a real-time data processing module may process the real-time power data, an offline data processing module may process the offline power data, and finally, a maximum demand regulation module may predict a required power of the load device at a first time point to obtain a target predicted power, and control a power supply state of the energy storage system based on the target predicted power.
In the embodiment of the invention, offline power data and real-time power data of load equipment of an energy storage system are acquired, wherein the offline power data are power data of the load equipment in a plurality of time periods acquired in advance, and the real-time power data are power data of the load equipment in a preset time window acquired in real time; predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after a preset time window; the power state of the energy storage system is controlled based on the target predicted power. According to the method and the device, through the offline power data and the real-time power data of the load equipment, the periodic electric energy demand power and the trending electric energy demand power of the load equipment at the first time point are predicted, then the target prediction power of the load equipment is predicted, the power supply state of the energy storage system is adjusted based on the target prediction power of the load equipment, the charging and discharging states of the energy storage equipment are accurately controlled based on the electric energy demand power of the load equipment, the electric energy is required to be supplied by the load equipment, the waste of electric energy caused by excessive supply of the energy storage system is avoided, the stable operation of a power grid is maintained, and the technical problem that the efficiency of the power supply state control of the energy storage system is lower is solved.
Optionally, predicting the required power of the load device at the first time point based on the offline power data and the real-time power data to obtain the target predicted power, including: predicting the required power of the load equipment at a first time point based on the offline power data to obtain first predicted power; predicting the required power of the load equipment at a first time point based on the real-time power data to obtain second predicted power; and obtaining the target predicted power based on the weighted sum of the first predicted power and the second predicted power.
The first predicted power may refer to the power of the periodic power demand of the load device at the first time point, and is denoted as X1 in this embodiment, the periodic power demand data at the time t+1 of day_1st.
The second predicted power may be the power of the power demand that needs to be predicted for the trend of the load device at the first time point, and is the trend of the power demand data at time t+1 of day_1st in this embodiment, denoted as X2.
In an alternative embodiment, when the first time point is the time t+1, the required power of the load device at the time t+1 may be predicted based on the offline power data, so as to obtain the periodic required amount X1 of the load device at the time t+1; the required power of the load equipment at the moment T+1 is predicted based on the real-time power data, and the real-time trend required quantity X2 of the load equipment at the moment T+1 is obtained; finally, based on the periodic demand X1 and the real-time trend demand X2 of the load equipment at the time T+1, the final demand value at the time T+1 can be obtained by a weighted average mode The calculation formula of the demand value at the time t+1 may be:
wherein X1 is periodic electric energy demand data at day_1st+1, and X2 is trending electric energy demand data at day_1st+1, in this embodiment, considering the effect of increasing the user's recent electricity consumption on demand, it is possible to setThe value is 0.7%>Weight is 0.3, specific parameter ∈ ->And->Can be adjusted according to the needs, and is not limited herein.
Optionally, predicting the required power of the load device at the first time point based on the offline power data, to obtain a first predicted power, including: dividing offline power data based on a plurality of time periods to obtain a data matrix; determining a plurality of historical powers corresponding to a second time point in the plurality of time periods based on the data matrix, wherein the second time point is a time point of the plurality of time periods corresponding to the first time point; and predicting the required power of the load equipment at a first time point based on the plurality of historical powers to obtain first predicted power.
In an alternative embodiment, the acquired periodic power consumption data of the load devices on different days at the same time may be stored in a matrix to obtain a data matrix, and fig. 3 is a schematic diagram of a data matrix of the power data of the alternative load devices according to an embodiment of the present invention, as shown in fig. 3, each row in the data matrix is used to represent the power consumption data of the load devices on different days at the same time, and each column in the data matrix is used to represent the power consumption data of the load devices on different days at the same time.
In an alternative embodiment, assuming that the periodic electric energy requirement data at time t+1 of day_1st day in the data matrix of fig. 3 is to be predicted, the data to be used in the prediction is monitoring data at the same time in the historical days, that is, the data points corresponding to H, H-1 and the like in fig. 3, that is, a plurality of historical powers corresponding to the second time point, in order to be able to identify the long-term periodic change of the power of the load device, the data of the first 3 months may be used for prediction, where the first-order smoothing calculation formula is as follows:
wherein,for the load device power value monitored at time t, a is a smoothing parameter, a ranges from [0,1]In between the two,is a smoothed value at time t, i.e. a predicted value at that time,/->The smoothed value at time t+1 is the predicted electric energy demand data at time t+1 on day 1.
In an alternative embodiment, it can be found that equation (1) is a recursive equation, which is in the form of:
in the formula (2), the last term y1 is an initial smoothed value, and may be replaced by an average value of first several monitored values in a normal case, or may be directly replaced by a 1 st monitored value, and in the technical scheme of the application, may be replaced by a first measured value.
Optionally, predicting the required power of the load device at the first time point based on the plurality of historical powers to obtain a first predicted power, including: determining a plurality of first sub-predicted powers based on the plurality of historical powers; and predicting the required power of the load equipment at a first time point based on the plurality of historical powers and the plurality of first sub-predicted powers to obtain first predicted power.
In an alternative embodiment, assuming that the periodic demand value at time t+1 in the data matrix of fig. 3 is to be predicted, the data at the same time in the historical days is shown in the following table 2, and table 2 is the truncated partial data:
in an alternative embodiment, the smoothing parameter a=0.5 may be taken, where the value of the smoothing parameter a may be adjusted according to the actual data distribution, and the specific calculation formula for predicting the periodic demand value at the time t+1 may be:
wherein, initial prediction data->The current first monitored value 2200 may be taken directly;
in an alternative embodiment, the prediction data of the final day_1, and the prediction data of the historical days, i.e. the power after the first order smoothing, are shown in table 3 below, and in particular, the smoothed value calculated at each time instant is the prediction value at the next time instant, where the prediction value of day_1 is x1= 2362.5.
Optionally, predicting the required power of the load device at the first time point based on the real-time power data to obtain the second predicted power, including: determining a power sequence of the load equipment in a preset time window based on the real-time power data, wherein the power sequence comprises a plurality of real-time powers acquired according to a time sequence; determining a plurality of second sub-predicted powers based on the plurality of real-time powers; and predicting the required power of the load equipment at the first time point based on the plurality of real-time powers and the plurality of second sub-predicted powers to obtain second predicted power.
The power sequence may refer to a sequence composed of real-time power data collected at a certain time interval, for example, the power sequence may refer to a sequence composed of real-time power data collected at a time interval of 1s within 15min, and the power sequence may be selected according to needs, which is not limited herein.
In an alternative embodiment, the Kafka message queue cluster in the data collection module may be docked by using a real-time stream processing framework, for example, spark stream processing (Apache Spark Streaming, simply referred to as Spark-Streaming), and the power sequence of the load device is collected in real time according to a preset time window of 15min and a time interval of 1s, where it may be assumed that the power sequence collected at time t+1 of day_1 is:
The power sequence corresponds to the data of the moment T, T-1 and the like in the data matrix of fig. 3.
In an alternative embodiment, the smoothing parameter a=0.5 may be taken, the initial predicted point takes the first observed value, and the first-order smoothing algorithm is used to refer to the calculation step of the first predicted power, so as to obtain the real-time trend predicted power at time t+1, and the power value is assumed to be X2.
Optionally, controlling the power state of the energy storage system based on the target predicted power includes: determining a target difference value between preset power and target predicted power of the energy storage system; controlling the power supply state to be switched to a charging state in response to the target predicted power being greater than or equal to the preset power, and charging the energy storage system based on the target difference value or the maximum charging power of the energy storage system; and controlling the power supply state to be switched to a discharge state in response to the target predicted power being smaller than the preset power, and discharging the energy storage system based on the target difference value or the maximum discharge power of the energy storage system.
The preset power may be the power supply of the energy storage system to the load device, that is, the maximum negotiated demand of the energy storage systemThe preset power may be set as needed, and is not limited herein.
The maximum charging power may be that the maximum charging power of the energy storage system isThe maximum charging power of the energy storage system is related to the type of the energy storage system, etc., and is not limited herein.
The maximum discharge power may be that the maximum discharge power of the energy storage system isThe maximum discharge power of the energy storage system is related to the type of the energy storage system, etc., and is not limited herein.
The target difference may refer to an absolute value of a difference between a preset power of the energy storage system and a target predicted power, and a calculation formula of the target difference may be expressed as:
target difference= |preset power-target predicted power|;
in an alternative embodiment, when the preset power of the energy storage system is greater than or equal to the target predicted power, the calculation formula of the target difference value may be expressed as:
target difference value= |preset power-target predicted power|=；
In another alternative embodiment, when the preset power of the energy storage system is smaller than the target predicted power, the calculation formula of the target difference value may be expressed as:
target difference value= |preset power-target predicted power|=；
In an alternative embodiment, when the energy storage system is at a predetermined powerGreater than or equal to the target predicted power Can be according to the maximum charging power +.>Or target difference->And charging the energy storage system.
In another alternative embodiment, when the energy storage system is at a predetermined powerLess than target predicted power +.>Can be according to the maximum discharge power +.>Or target difference->And discharging the energy storage system.
Optionally, charging the energy storage system based on the target difference or the maximum charging power of the energy storage system includes: in response to the target difference being greater than or equal to the maximum charging power, charging the energy storage system based on the maximum charging power; and in response to the target difference being less than the maximum charging power, charging the energy storage system based on the target difference.
In an alternative embodiment, when the preset power of the energy storage system is greater than or equal to the target predicted power, and the target difference valueGreater than or equal to the maximum charging power +.>In this case, the maximum charging power can be set>And charging the energy storage system.
In another alternative embodiment, when the preset power of the energy storage system is greater than or equal to the target predicted power, and the target difference valueLess than maximum charging power->At this time, the difference value can be set to be +.>And charging the energy storage system.
Optionally, discharging the energy storage system based on the target difference or the maximum discharge power of the energy storage system, including: discharging the energy storage system based on the target difference value in response to the target difference value being less than the maximum discharge power; and in response to the target difference being greater than or equal to the maximum charge power, discharging the energy storage system based on the maximum discharge power.
In an alternative embodiment, when the preset power of the energy storage system is less than the target predicted power, and the target difference valueLess than maximum discharge power->At this time, the difference value can be set to be +.>And discharging the energy storage system.
In another alternative embodiment, when the preset power of the energy storage system is less than the target predicted power, and the target difference valueGreater than or equal to the maximum discharge power +.>At this time, the maximum discharge power can be set>And discharging the energy storage system.
In an alternative embodiment, fig. 4 is a schematic diagram illustrating an adjustment effect of a power supply state control method of an alternative energy storage system according to an embodiment of the present invention, as shown in fig. 4, an abscissa of the coordinate axes of fig. 4 represents a time T, in units: the ordinate of the coordinate axis represents the power P, the demand curve adjusted by the power supply state control method of the energy storage system in this embodiment is curve 1 in fig. 4, the unadjusted actual demand curve of the user is curve 2 in fig. 4, the negotiated maximum demand is curve 3 in fig. 4, and it can be seen from fig. 4 that the regulated demand is close to the actual use demand of the user, and the demand can be prevented from exceeding the negotiated maximum demand.
In an optional embodiment, the invention provides a method and a system for predicting electricity consumption based on large-scale load equipment operation data, wherein a specific prediction algorithm is a first-order exponential smoothing algorithm, real-time demand prediction is realized by reducing time and space complexity, and based on predicted demand data, the maximum demand control is performed on a user electricity consumption system by combining an energy storage system, so that the influence on the load equipment operated in production is reduced.
Example 2
According to another aspect of the embodiments of the present invention, a power supply state control device for an energy storage system is provided, where the device may execute the power supply state control method for the energy storage system in the foregoing embodiments, and a specific implementation method and a preferred application scenario are the same as those in the foregoing embodiments, and are not described herein again.
Fig. 5 is a power supply state control device of an energy storage system according to an embodiment of the present application, as shown in fig. 5, the device includes the following: an acquisition module 502, a prediction module 504, a control module 506.
The acquiring module 502 is configured to acquire offline power data and real-time power data of a load device of the energy storage system, where the offline power data is power data of the load device acquired in advance in a plurality of time periods, and the real-time power data is power data of the load device acquired in real time in a preset time window; the prediction module 504 is configured to predict a required power of the load device at a first time point based on the offline power data and the real-time power data, to obtain a target predicted power, where the first time point is a time point after a preset time window; a control module 506 for controlling a power state of the energy storage system based on the target predicted power.
In the above embodiments of the present application, the prediction module includes: the device comprises a first prediction unit, a second prediction unit and a first determination unit.
The first prediction unit is used for predicting the required power of the load equipment at a first time point based on the offline power data to obtain first predicted power; the second prediction unit is used for predicting the required power of the load equipment at the first time point based on the real-time power data to obtain second predicted power; the first determination unit is used for obtaining target predicted power based on a weighted sum of the first predicted power and the second predicted power.
In the above embodiment of the present application, the first prediction unit includes: the method comprises a dividing subunit, a first determining subunit and a first predicting subunit.
The dividing subunit is used for dividing the offline power data based on a plurality of time periods to obtain a data matrix; the first determining subunit is configured to determine, based on the data matrix, a plurality of historical powers corresponding to a second time point in the plurality of time periods, where the second time point is a time point in the plurality of time periods corresponding to the first time point; the first prediction subunit is used for predicting the required power of the load equipment at a first time point based on the plurality of historical powers to obtain first predicted power.
Wherein the prediction subunit is configured to determine a plurality of first sub-predicted powers based on the plurality of historical powers; and predicting the required power of the load equipment at a first time point based on the plurality of historical powers and the plurality of first sub-predicted powers to obtain first predicted power.
In the above embodiment of the present application, the second prediction unit includes: the second determining subunit, the third determining subunit and the second predicting subunit.
The second determining subunit is used for determining a power sequence of the load equipment in a preset time window based on the real-time power data, wherein the power sequence comprises a plurality of real-time powers acquired according to a time sequence; the third determining subunit is configured to determine a plurality of second sub-predicted powers based on the plurality of real-time powers; the second prediction subunit is configured to predict, based on the plurality of real-time powers and the plurality of second sub-prediction powers, a required power of the load device at the first time point, to obtain a second prediction power.
In the above embodiments of the present application, the control module includes: the system comprises a second determining unit, a first control unit and a second control unit.
The second determining unit is used for determining a target difference value between preset power and target predicted power of the energy storage system; the first control unit is used for controlling the power supply state to be switched to the charging state in response to the fact that the target predicted power is larger than or equal to the preset power, and charging the energy storage system based on the target difference value or the maximum charging power of the energy storage system; the second control unit is used for controlling the power supply state to be switched into the discharge state in response to the target predicted power being smaller than the preset power, and discharging the energy storage system based on the target difference value or the maximum discharge power of the energy storage system.
In the above embodiment of the present application, the first control unit includes: the first charging subunit and the second charging subunit.
The first charging subunit is used for charging the energy storage system based on the maximum charging power in response to the target difference value being greater than or equal to the maximum charging power; the second charging subunit is configured to charge the energy storage system based on the target difference value in response to the target difference value being less than the maximum charging power.
In the above embodiment of the present application, the second control unit includes: a first discharge subunit and a second discharge subunit.
The first discharging subunit is used for discharging the energy storage system based on the target difference value in response to the target difference value being smaller than the maximum discharging power; the second discharging subunit is used for discharging the energy storage system based on the maximum charging power in response to the target difference value being greater than or equal to the maximum charging power.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a computer readable storage medium, including a stored program, where the power supply state control method of the energy storage system described above is executed in a processor of a device where the program is controlled to run.
The computer storage medium in the above steps may be a medium for storing a certain discrete physical quantity in a computer memory, and the computer storage medium mainly includes a semiconductor, a magnetic core, a magnetic drum, a magnetic tape, a laser disk, and the like. The computer readable storage medium may include a stored program which may be a set of instructions which can be recognized and executed by a computer, running on an electronic computer, and which may be an informative tool for meeting certain needs of a person.
Example 4
According to another aspect of embodiments of the present invention, there is also provided an electronic device, one or more processors; a storage means for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors are caused to perform the method for controlling the power state of the energy storage system.
The memory device in the above steps may be a kind of sequential logic circuit, and is used for storing memory components such as data and instructions, and is mainly used for storing programs and data; a processor may be a functional unit that interprets and executes instructions, and has a unique set of operating commands, which may be referred to as the processor's instruction set, as memory, call-in, etc.; the storage device stores a computer program, which can be a set of instructions that can be identified and executed by a computer, and an informatization tool that runs on an electronic computer and meets certain demands of people.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A method for controlling a power state of an energy storage system, comprising:
acquiring offline power data and real-time power data of load equipment of an energy storage system, wherein the offline power data are power data, acquired in advance, of the load equipment in a plurality of time periods, and the real-time power data are power data, acquired in real time, of the load equipment in a preset time window;
predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after the preset time window;
controlling a power state of an energy storage system based on the target predicted power;
the method for predicting the required power of the load device at the first time point based on the offline power data and the real-time power data to obtain the target predicted power includes: predicting the required power of the load equipment at the first time point based on the offline power data to obtain first predicted power; predicting the required power of the load equipment at the first time point based on the real-time power data to obtain second predicted power; and obtaining the target predicted power based on a weighted sum of the first predicted power and the second predicted power.
2. The method of claim 1, wherein predicting the required power of the load device at the first point in time based on the offline power data results in a first predicted power, comprising:
dividing the offline power data based on a plurality of time periods to obtain a data matrix;
determining a plurality of historical powers corresponding to a second time point in the plurality of time periods based on the data matrix, wherein the second time point is a time point of the plurality of time periods corresponding to the first time point;
and predicting the required power of the load equipment at the first time point based on the plurality of historical powers to obtain the first predicted power.
3. The method of claim 2, wherein predicting the required power of the load device at the first point in time based on the plurality of historical powers, the first predicted power comprising:
determining a plurality of first sub-predicted powers based on the plurality of historical powers;
and predicting the required power of the load equipment at the first time point based on the historical powers and the first sub-predicted powers to obtain the first predicted power.
4. The method of claim 1, wherein predicting the required power of the load device at the first point in time based on the real-time power data, to obtain a second predicted power, comprises:
determining a power sequence of the load equipment in the preset time window based on the real-time power data, wherein the power sequence comprises a plurality of real-time powers acquired according to a time sequence;
determining a plurality of second sub-predicted powers based on the plurality of real-time powers;
and predicting the required power of the load equipment at the first time point based on the real-time powers and the second sub-predicted powers to obtain the second predicted power.
5. The method of claim 1, wherein controlling the power state of the energy storage system based on the target predicted power comprises:
determining a target difference value between the preset power of the energy storage system and the target predicted power;
controlling the power supply state to be switched to a charging state in response to the target predicted power being greater than or equal to the preset power, and charging the energy storage system based on the target difference value or the maximum charging power of the energy storage system;
And controlling the power supply state to be switched to a discharge state in response to the target predicted power being smaller than the preset power, and discharging the energy storage system based on the target difference value or the maximum discharge power of the energy storage system.
6. The method of claim 5, wherein charging the energy storage system based on the target difference or a maximum charging power of the energy storage system comprises:
charging the energy storage system based on the maximum charging power in response to the target difference being greater than or equal to the maximum charging power;
and in response to the target difference being less than the maximum charging power, charging the energy storage system based on the target difference.
7. The method of claim 5, wherein discharging the energy storage system based on the target difference or a maximum discharge power of the energy storage system comprises:
discharging the energy storage system based on the target difference value in response to the target difference value being less than the maximum discharge power;
and in response to the target difference being greater than or equal to the maximum charging power, discharging the energy storage system based on the maximum charging power.
8. A power state control device of an energy storage system, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring offline power data and real-time power data of load equipment of an energy storage system, wherein the offline power data are power data of the load equipment in a plurality of time periods acquired in advance, and the real-time power data are power data of the load equipment in a preset time window acquired in real time;
the prediction module is used for predicting the required power of the load equipment at a first time point based on the offline power data and the real-time power data to obtain target predicted power, wherein the first time point is a time point after the preset time window;
the control module is used for controlling the power supply state of the energy storage system based on the target predicted power;
wherein the prediction module comprises:
the first prediction unit is used for predicting the required power of the load equipment at the first time point based on the offline power data to obtain first predicted power;
the second prediction unit is used for predicting the required power of the load equipment at the first time point based on the real-time power data to obtain second predicted power;
And the first determining unit is used for obtaining the target predicted power based on the weighted sum of the first predicted power and the second predicted power.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the power supply state control method of the energy storage system of any one of claims 1 to 7 is executed in a processor of a device in which the program is controlled to run.
10. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the power state control method of the energy storage system of any of claims 1-7.
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